Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression (1802.06816v1)

Abstract: The rapidly growing body of research in adversarial machine learning has demonstrated that deep neural networks (DNNs) are highly vulnerable to adversarially generated images. This underscores the urgent need for practical defense that can be readily deployed to combat attacks in real-time. Observing that many attack strategies aim to perturb image pixels in ways that are visually imperceptible, we place JPEG compression at the core of our proposed Shield defense framework, utilizing its capability to effectively "compress away" such pixel manipulation. To immunize a DNN model from artifacts introduced by compression, Shield "vaccinates" a model by re-training it with compressed images, where different compression levels are applied to generate multiple vaccinated models that are ultimately used together in an ensemble defense. On top of that, Shield adds an additional layer of protection by employing randomization at test time that compresses different regions of an image using random compression levels, making it harder for an adversary to estimate the transformation performed. This novel combination of vaccination, ensembling, and randomization makes Shield a fortified multi-pronged protection. We conducted extensive, large-scale experiments using the ImageNet dataset, and show that our approaches eliminate up to 94% of black-box attacks and 98% of gray-box attacks delivered by the recent, strongest attacks, such as Carlini-Wagner's L2 and DeepFool. Our approaches are fast and work without requiring knowledge about the model.

• The paper demonstrates that JPEG compression effectively removes adversarial noise by discarding high-frequency components.
• It shows that retraining models with JPEG-compressed images—termed 'vaccination'—significantly enhances network robustness.
• Experiments on ImageNet reveal that an ensemble of vaccinated models can neutralize up to 94% of black-box and 98% of gray-box attacks.

Analysis of the Shield Defense Mechanism Utilizing JPEG Compression in Deep Learning

Deep neural networks (DNNs) have garnered immense success across various applications, yet they remain critically vulnerable to adversarial attacks. Such attacks manipulate images in a manner imperceptible to human eyes, misleading DNNs into incorrect predictions. The paper "Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression" proposes an innovative defense framework, Shield, which leverages JPEG compression to mitigate these vulnerabilities.

The Shield framework incorporates a multi-pronged approach, utilizing JPEG compression as a core defense mechanism. JPEG’s salient feature of reducing high-frequency components, which often constitute adversarial noise, becomes pivotal here. By deploying systematic JPEG compression, even moderate compression levels can achieve significant mitigation of adversarial perturbations without adversely affecting the model’s performance on benign images.

Key Contributions and Insights

1. Compression-Based Preprocessing: The research emphasizes JPEG compression’s effectiveness by demonstrating that it can sanitize adversarial inputs before their processing by the neural network. Given that many attacks introduce high-frequency noise, JPEG's inherent property of discarding such frequencies proves beneficial.
2. Model Vaccination: Shield innovates further by retraining models with JPEG-compressed images—termed as ‘vaccination’—enhancing model robustness against potential compression artifacts. They developed multiple “vaccinated” models using various compression levels, each contributing to an ensemble defense strategy.
3. Ensemble and Randomization: An ensemble of models, each “vaccinated” with varying compression-level images, significantly boosts the defense system’s robustness. Shield adds an additional security layer through stochastic local quantization (SLQ), introducing randomness by varying the compression levels across image segments—complicating adversarial attempts significantly.
4. Empirical Evidence: Extensive experiments on the ImageNet dataset reveal that Shield can neutralize up to 94% of black-box and 98% of gray-box attacks, including formidable adversarial strategies like Carlini-Wagner L2 and DeepFool. Such empirical evaluation underscores Shield’s efficacy and highlights its practical deployability in real-time environments.

Implications and Future Developments

The Shield defense framework demonstrates practical and computationally efficient solutions against adversarial machine learning threats. Its reliance on ubiquitous JPEG compression allows it to seamlessly integrate into existing systems without necessitating extensive modifications or introducing prohibitive computational costs.

From a theoretical standpoint, Shield elucidates the potential of utilizing established data processing techniques, such as compression, as robust defenses against adversarial inputs. This approach could inspire further research into leveraging other traditional techniques within the domain of adversarial defense.

Looking forward, Shield underscores the need for a composite defense strategy incorporating multi-faceted approaches, combining traditional image processing techniques with novel deep learning methodologies. Such integrated methodologies can potentially outpace the evolving landscape of adversarial attacks, enhancing both the resilience and applicability of DNNs across security-sensitive domains.

In conclusion, the paper presents a compelling argument for the adoption of JPEG compression in deep learning defense frameworks. The Shield methodology not only addresses pressing vulnerabilities in deep networks but sets a precedent for future exploration into optimizing and integrating auxiliary processing techniques for enhanced adversarial robustness.